Ahmed Sh., Alshater M., Ammari A., Hammami H., Artificial intelligence and machine learning in finance: A bibliometric review, Research in International Business and Finance, 61, 2022,
https://doi.org/10.1016/j.ribaf.2022.101646.
Almamy, J., Aston, J., & Ngwa, L., An evaluation of Altman’s Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK. Journal of Corporate Finance, 36, 2016, pp.278–285.
https://doi.org/10.1016/j.jcorpfin.201
Altman, E., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 4, 1968, pp. 589-609
Black F., Scholes M., The pricing of options and corporate liabilities, Journal of Political Economy, 81, 3, 1973, pp. 637-654
Bock K., Coussement K., Lessmann S., Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach, European Journal of Operational Research, 285 (20), 2020, pp. 612-630,
https://doi.org/10.1016/j.ejor.2020.01.052.
Borchert P., Coussement K., Caigny A., Weerdt J., Extending business failure prediction models with textual website content using deep learning, European Journal of Operational Research, 306, 2023, pp. 348-357
Bragoli D., Ferretti C., Ganugi P. & Marseguerra G., Mezzogori D., Zammori F., “Machine-learning models for bankruptcy prediction: do industrial variables matter?,” Spatial Economic Analysis, Taylor & Francis Journals, 17 (2), 2022, pp. 156-177
Chatterjee S., Khan P., Byun Y., Recent advances and applications of machine learning in the variable renewable energy sector, Energy Reports, 12, 2024, pp. 5044-5065
Du X., Li W., Ruan S., Li L., CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection, Applied Soft Computing, 97, 2020,
https://doi.org/ 10.1016/j.asoc.2020.106758.
Ékes, K. S., & Koloszár, L., The efficiency of bankruptcy forecast models in the Hungarian SME Sector. Journal of Competitiveness, 6(2), 2014, pp. 56–73.
https://doi.org/10.7441/joc.2014.02.05
F1 score for imbalanced data, F1 Score in Machine Learning Explained | Encord
Fernández-Gámez M.Á., Soria J., Santos J., Alaminos D., European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors, Economic Modelling, 88, 2020, pp. 398-407
Fitzpatrick P., A comparison of the ratios of successful industrial enterprises with those of failed companies, The Certified Public Accountant, 12, 1932, pp. 727-731, 598-605, 656-662 respectively
Hwang, R. C., Cheng, K. F., & Lee, C. F., On multiple-class prediction of issuer credit ratings. Applied Stochastic Models in Business and Industry, 25(5), 2009, pp.535–550,
https://doi.org/10.1002/asmb.735
Jabeur S., Stef N., Carmona P., 2023, Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering,” Computational Economics, Springer; Society for Computational Economics, vol. 61(2), pp. 715-741
James G., Witten D., Hastie T., Tibshirani R.: “An Introduction to Statistical Learning: with Applications in R”, Springer, Second Edition 2021,
https://www.statlearning.com/
Kalak I., Azevedo A., Hudson R., Karim M., Stock liquidity and SMEs’ likelihood of bankruptcy: Evidence from the US market, Research in International Business and Finance, 42, 2017, pp. 1383-1393,
https://doi.org/10.1016/j.ribaf.2017.07.077.
Karas, M., Reznakova, M., Bartos, V., & Zinecker, M., Possibilities for the application of the Altman model within the Czech Republic, In Recent Researches in Law Science and Finances, 2023, pp. 203–207,
http://www.wseas.us/e-library/conferences/2013/Chania/ICFA/ICFA-30.pdf
Ko, Y. C., Fujita, H., & Li, T., An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies, Applied Soft Computing, 52, 2017, 748–759,
https://doi.org/10.1016/j.asoc.2016.09.050
Kooptiwoot, S. & Kooptiwoot, S. & Javadi, B., Application of regression decision tree and machine learning algorithms to examine students’ online learning preferences during COVID-19 pandemic. International Journal of Education and Practice. 12, 2024, pp. 82-94,
https://doi.org/10.18488/61.v12i1.3619. (a)
Kooptiwoot, S. & Tharasawatpipat, Ch. & Choo-In, S. & Kayee, P. & Javadi, B., AI-driven telemedicine: Optimizing daily dietary recommendations amidst the COVID-19 pandemic. Journal of Infrastructure, Policy and Development. 8, 2024,
https://doi/org/10.24294/jipd.v8i11.8908. (b)
Kooptiwoot,S. & Tharasawatpipat, Ch.& Choo-in, S. & Kayee, P. & Meethongjan, K. & Sangsuwon, Ch. & Javadi, B., Deciphering the complexity of COVID-19 transmission: Unveiling precision through robust vaccination policies and advanced predictive modeling with random forest regression, Journal of Infrastructure, Policy and Development, 8, 2024,
https://doi.org/10.24294/jipd.v8i8.5321. (c)
Kou G., Xu Y., Yi Peng, Shen F., Chen Y., Chang K., Kou S., Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection, Decision Support Systems, 140, 2021,
https://doi.org/10.1016/j.dss.2020.113429.
Liang D., Lu C., Tsai C., Shih G., Financial ratios and corporate governance indicators in bankruptcy prediction: a comprehensive study, Eur. J. Oper. Res., 252 (2), 2016, pp. 561-572,
https://doi.org/10.1016/j.ejor.2016.01.012
Liang D., Tsai C., Wu H., The effect of feature selection on financial distress prediction, Knowledge-Based Systems, 73, 2015, pp. 289.
Logistic Regression in Python, sklearn.linear_model.LogisticRegression — scikit-learn 1.3.2 documentation
Lohmann Ch., Möllenhoff S., How do bankruptcy risk estimations change in time? Empirical evidence from listed US companies, Finance Research Letters, Volume 58, Part B, 2023,
https://doi.org/10.1016/j.frl.2023.104389.
Mateika, H., Jia, J., Lillard, L., Cronbaugh, N., & Shin, W., Fallen angel bonds investment and bankruptcy predictions using manual models and automated machine learning, 2022, arXiv preprint arXiv:2212.03454.
Odom M., Sharda R., A neural network model for bankruptcy prediction, Proceedings of the IJCNN International Joint Conference on Neural Networks, IEEE, 1990, pp. 163-168
Ohlson J., Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18 (1), 1980, pp. 109-131
Pereira J.M., Basto M., Silva A., The logistic lasso and ridge regression in predicting corporate failure, Procedia Economics and Finance, 39, 2016, pp. 634.
Ruxanda, G., Zamfir, C., & Muraru, A., Predicting financial distress for Romanian companies. Technological and Economic Development Economy, 24(6), 2018, 2318–2337.
https://doi.org/10.3846/tede.2018.6736
Sarkar S., Sriram R., Bayesian models for early warning of bank failures, Management Science, 47 (11), 2001, pp. 1457-1475
Shen F., Liu Y., Wang R., Zhou W., A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment, Knowledge-Based Systems, 192, 2020,
https://doi.org/10.1016/j.knosys.2019.105365.
SVM In Python, sklearn.svm.SVC — scikit-learn 1.3.2 documentation
Szeghalmy S, Fazekas A. A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors (Basel)., 23(4), 2023,
https://doi.org/10.3390/s23042333.
Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K., Financial crisis prediction model using ant colony optimization. International Journal of Information Management, 50, 2020, pp.538-556.
Veganzones, D. and Severin, E., “Corporate failure prediction models in the twenty-first century: a review”, European Business Review, 33 (2), 2021, pp. 204-226.
https://doi.org/10.1108/EBR-12-2018-0209
Voda, A. D., Dobrotă, G., Țîrcă, D. M., Dumitrașcu, D. D., & Dobrotă, D., Corporate bankruptcy and insolvency prediction model . Technological and Economic Development of Economy, 27(5), 2021, pp. 1039-1056,
https://doi.org/10.3846/tede.2021.15106
Yuxia S., Congyuan Y., Zhiya L., Yanting T., Initiative for China to establish a dual model of mixed corporate governance on bankruptcy reorganization: An empirical analysis based on 93 listed companies, Heliyon, 8(12), 2022,
https://doi.org/10.1016/j.heliyon.2022.e12007.
Zelenkov Y., Fedorova E., Chekrizov D., Two-step classification method based on genetic algorithm for bankruptcy forecasting, Expert Systems with Applications, 88, 2017, pp. 393.
Zhao J., Ouenniche J., Smedt J., Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction, Machine Learning with Applications, 15, 2024,
https://doi.org/10.1016/j.mlwa.2024.100527.
Zhou L., Lu D., Fujita H., The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches, Knowledge-Based Systems, 85, 2015, pp. 52-61.
